The field of the invention is systems and methods for magnetic resonance imaging (“MRI”). More particularly, the invention relates to systems and methods for parallel MRI data acquisition and image reconstruction.
MRI uses the nuclear magnetic resonance (“NMR”) phenomenon to produce images. When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the nuclei in the tissue attempt to align with this polarizing field, but precess about it at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a radio frequency (“RF”) magnetic field (excitation field B1) that is in the x-y plane and that is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or “tipped,” into the x-y plane to produce a net transverse magnetic moment Mxy. A signal is emitted by the excited nuclei or “spins,” after the excitation signal B1 is terminated, and this signal may be received and processed to form an image.
When utilizing these “MR” signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received MR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
The measurement cycle used to acquire each MR signal is performed under the direction of a pulse sequence produced by a pulse sequencer. Clinically available MRI systems store a library of such pulse sequences that can be prescribed to meet the needs of many different clinical applications. Research MRI systems include a library of clinically-proven pulse sequences and they also enable the development of new pulse sequences.
The MR signals acquired with an MRI system are signal samples of the subject of the examination in Fourier space, or what is often referred to in the art as “k-space.” Each MR measurement cycle, or pulse sequence, typically samples a portion of k-space along a sampling trajectory characteristic of that pulse sequence. Most pulse sequences sample k-space in a raster scan-like pattern sometimes referred to as a “spin-warp,” a “Fourier,” a “rectilinear,” or a “Cartesian” scan. The spin-warp scan technique employs a variable amplitude phase encoding magnetic field gradient pulse prior to the acquisition of MR spin-echo signals to phase encode spatial information in the direction of this gradient. In a two-dimensional implementation (“2DFT”), for example, spatial information is encoded in one direction by applying a phase encoding gradient, Gy, along that direction, and then a spin-echo signal is acquired in the presence of a readout magnetic field gradient, Gx, in a direction orthogonal to the phase encoding direction. The readout gradient present during the spin-echo acquisition encodes spatial information in the orthogonal direction. In a typical 2DFT pulse sequence, the magnitude of the phase encoding gradient pulse, Gy, is incremented, ΔGy, in the sequence of measurement cycles, or “views” that are acquired during the scan to produce a set of k-space MR data from which an entire image can be reconstructed.
There are many other k-space sampling patterns used by MRI systems. These include “radial”, or “projection reconstruction” scans in which k-space is sampled as a set of radial sampling trajectories extending from the center of k-space. The pulse sequences for a radial scan are characterized by the lack of a phase encoding gradient and the presence of a readout gradient that changes direction from one pulse sequence view to the next. There are also many k-space sampling methods that are closely related to the radial scan and that sample along a curved k-space sampling trajectory rather than the straight line radial trajectory.
Depending on the technique used, many MR scans currently require many minutes to acquire the necessary data used to produce medical images. The reduction of this scan time is an important consideration, since reduced scan time increases patient throughout, improves patient comfort, and improves image quality by reducing motion artifacts. Many different strategies have been developed to shorten the scan time.
One such strategy is referred to generally as “parallel MRI” (“pMRI”). Parallel MRI techniques use spatial information from arrays of radio frequency (“RF”) receiver coils to substitute for the spatial encoding that would otherwise have to be obtained in a sequential fashion using RF pulses and magnetic field gradients, such as phase and frequency encoding gradients. Each of the spatially independent receiver coils of the array carries certain spatial information and has a different spatial sensitivity profile. This information is utilized in order to achieve a complete spatial encoding of the received MR signals, for example, by combining the simultaneously acquired data received from each of the separate coils. Parallel MRI techniques allow an undersampling of k-space, in general, by reducing the number of acquired phase-encoded k-space sampling lines, while keeping the maximal extent covered in k-space fixed. The combination of the separate MR signals produced by the separate receiver coils enables a reduction of the acquisition time required for an image, in comparison to a conventional k-space data acquisition, by a factor related to the number of the receiver coils. Thus the use of multiple receiver coils acts to multiply imaging speed, without increasing gradient switching rates or RF power.
Two categories of such parallel imaging techniques that have been developed are the so-called “image space methods” and “k-space methods.” An exemplary image space method is known in the art as sensitivity encoding (“SENSE”), while an exemplary k-space method is known in the art as simultaneous acquisition of spatial harmonics (“SMASH”). With SENSE, the undersampled k-space data is first Fourier transformed to produce an aliased image from each coil, and then the aliased image signals are unfolded by a linear transformation of the superimposed pixel values. With SMASH, the omitted k-space lines are synthesized or reconstructed prior to Fourier transformation, by constructing a weighted combination of neighboring k-space lines acquired by the different receiver coils. SMASH requires that the spatial sensitivity of the coils be determined, and one way to do so is by “autocalibration” that entails the use of variable density k-space sampling.
A more recent advance to the SMASH technique that uses autocalibration is a technique known as generalized autocalibrating partially parallel acquisitions (“GRAPPA”). With GRAPPA, k-space lines near the center of k-space are sampled at the Nyquist frequency, while k-space lines in the peripheral regions of k-space are acquired with a degree of undersampling. The center k-space lines are referred to as so-called autocalibration signal (“ACS”) lines, which are used to determine weighting factors that are utilized to synthesize, or reconstruct, the missing k-space lines. In particular, a linear combination of individual coil data is used to create the missing lines of k-space. The coefficients for the combination are determined by fitting the acquired data to the more highly sampled data near the center of k-space.
Image reconstruction from data acquired with pMRI techniques may require an implicit or explicit estimation of the sensitivity profile, or “map,” of the RF coil elements used to acquire the data. However, accurate coil sensitivity map estimation is practically difficult. Indeed, errors in the estimated coil sensitivity maps can propagate to the reconstructed images. To mitigate this challenge, ACS lines can be utilized, such as those used in GRAPPA and other methods such as AUTO-SMASH and PILS. These methods empirically measure a small portion of the fully gradient-encoded data in order to estimate the necessary coefficients to reconstruct, or synthesize,” missing data in the accelerated scans. The coil sensitivity information embedded in the ACS lines is implicitly used in the image reconstruction. Therefore, the reconstructed images are more robust to errors related to performing an explicit estimation of the coil sensitivity maps. Furthermore, GRAPPA and PILS can achieve coil-by-coil image reconstructions. Individual coil images can then be later combined in different ways to achieve the optimal performance.
In general, increasing the number of channels in an RF coil array can further improve the spatiotemporal resolution achievable with pMRI techniques. Previously, magnetic resonance inverse imaging (“InI”) was developed to achieve ultra-fast functional MRI (“fMRI”) of the human brain during the performance of functional tasks as described, for example, in U.S. Pat. No. 7,394,251, which is herein incorporated by reference in its entirety.
Mathematically, InI generalizes pMRI reconstructions from an over-determined linear system to an under-determined linear system in order to reduce the time required for k-space traversal and, therefore, to achieve a significant improvement in temporal resolution. Previous InI reconstructions employed minimum-norm estimates (“MNE”) or linear-constraint minimum variance (“LCMV”) beamformer spatial filtering in image space. These two methods can be viewed as the generalization of the SENSE pMRI technique with minimally gradient-encoded data. To reveal relative changes in task-related fMRI using the so-called “in vivo sensitivity” approach described by (Sodickson, 2000), coil sensitivity maps can be empirically measured by collecting fully gradient-encoded data. However, individual coil images cannot be reconstructed at each time instant in the InI acquisition since an explicit coil sensitivity map is not measured.